The dynamics of reinforcement learning in cooperative multiagent systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Economic dynamics of agents in multiple auctions
Proceedings of the fifth international conference on Autonomous agents
Multiagent learning using a variable learning rate
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
On Multiagent Q-Learning in a Semi-Competitive Domain
IJCAI '95 Proceedings of the Workshop on Adaption and Learning in Multi-Agent Systems
Pricing and Resource Allocation in Computational Grid with Utility Functions
ITCC '05 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II - Volume 02
Analyzing Market-Based Resource Allocation Strategies for the Computational Grid
International Journal of High Performance Computing Applications
A taxonomy of market-based resource management systems for utility-driven cluster computing
Software—Practice & Experience
Reinforcement learning with utility-aware agents for market-based resource allocation
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Learning the IPA Market with Individual and Social Rewards
IAT '07 Proceedings of the 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Predicting and preventing coordination problems in cooperative Q-learning systems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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Market-based mechanisms offer a promising approach for distributed resource allocation. Machine Learning has been proposed to influence and optimize market-based resource allocation. In particular, Reinforcement Learning (RL) has been used to improve the allocation in terms of utility received by resource requesting agents in the Iterative Price Adjustment (IPA) mechanism. This paper analyses the individual and social behaviour of agents in the IPA market-based resource allocation with RL. In particular, it presents results of experimental investigation on the influences of the amount of learning in the agents' behaviour aiming at determining how much learning is sufficient and the theoretical-experimental explanation of the agents' behaviours using game theory.